Comparison of methods for the detection and extrapolation of trends in groundwater quality.

Land use changes and the intensification of agriculture since the 1950s have resulted in a deterioration of groundwater quality in many European countries. For the protection of groundwater quality, it is necessary to (1) assess the current groundwater quality status, (2) detect changes or trends in groundwater quality, (3) assess the threat of deterioration and (4) predict future changes in groundwater quality. A variety of approaches and tools can be used to detect and extrapolate trends in groundwater quality, ranging from simple linear statistics to distributed 3D groundwater contaminant transport models.

A refined lack-of-fit statistic to calibrate pesticide fate models for responsive systems.

Background: Calibration by inverse modelling was performed with the MACRO transport and fate model using long-term (>10 years) drainflow and isoproturon (IPU) data from western France. Two lack-of-fit (LOF) indices were used to control the inverse modelling: sum of squares (SS) and an alternative statistic called the vertical-horizontal distance integrator (VHDI), which is designed to account for offsets in observed and predicted arrival times of peak IPU concentration. With these data, SS was artificially inflated because it is limited to comparison of predicted and observed IPU concentrations that are concurrent in time.

Stationary and non-stationary autoregressive processes with external inputs for predicting trends in water quality.

An autoregressive approach for the prediction of water quality trends in systems subject to varying meteorological conditions and short observation periods is discussed. Under these conditions, the dynamics of the system can be reliably forecast, provided their internal processes are understood and characterized independently of the external inputs. A methodology based on stationary and non-stationary autoregressive processes with external inputs (ARX) is proposed to assess and predict trends in hydrosystems which are at risk of contamination by organic and inorganic pollutants, such as pesticides or nutrients.

Identification of key climatic factors regulating the transport of pesticides in leaching and to tile drains.

Background: Key climatic factors influencing the transport of pesticides to drains and to depth were identified. Climatic characteristics such as the timing of rainfall in relation to pesticide application may be more critical than average annual temperature and rainfall. The fate of three pesticides was simulated in nine contrasting soil types for two seasons, five application dates and six synthetic weather data series using the MACRO model, and predicted cumulative pesticide loads were analysed using statistical methods.

Formation of pesticide nonextractable (bound) residues in soil: magnitude, controlling factors and reversibility.

The analysis of the coherent data on nonextractable (bound) residues (NER) from the literature and EU pesticide registration dossiers allows the identification of general trends, in spite of the large variability and heterogeneity of data. About 50% of the pesticides reviewed exhibit a low proportion of NER (less than 30% of the initial amount) while only 12% of pesticides have a proportion of NER exceeding 70%. The lowest proportion of NER was found for dinitroanilines (<20%), and the largest value was obtained for carbamates, and in particular dithiocarbamates.

User subjectivity in Monte Carlo modeling of pesticide exposure.

Monte Carlo techniques are increasingly used in pesticide exposure modeling to evaluate the uncertainty in predictions arising from uncertainty in input parameters and to estimate the confidence that should be assigned to the modeling results. The approach typically involves running a deterministic model repeatedly for a large number of input values sampled from statistical distributions. In the present study, six modelers made choices regarding the type and parameterization of distributions assigned to degradation and sorption data for an example pesticide, the correlation between the parameters, the tool and method used for sampling, and the number of samples generated.

Inverse modelling for estimating sorption and degradation parameters for pesticides.

The leaching model PESTRAS was used to estimate sorption and degradation values for bentazone from three lysimeter datasets using the inverse modelling package PEST. Investigations were undertaken to assess the influence on calibration results of (1) values attributed to uncertain parameters not included in the calibration, and (2) starting values supplied to the inverse modelling package. Automatic calibrations with different realistic values for the Freundlich exponent n(f) yielded different combinations of K(om) and DT50.

Exposure to sulfosulfuron in agricultural drainage ditches: field monitoring and scenario-based modelling.

Field monitoring and scenario-based modelling were used to assess exposure of small ditches in the UK to the herbicide sulfosulfuron following transport via field drains. A site in central England on a high pH, clay soil was treated with sulfosulfuron, and concentrations were monitored in the single drain outfall and in the receiving ditch 1 km downstream. Drainflow in the nine months following application totalled 283 mm.

Issues of replicability in Monte Carlo modeling: a case study with a pesticide leaching model.

Sensitivity and uncertainty analyses based on Monte Carlo sampling were undertaken for various numbers of runs of the pesticide leaching model (PELMO). Analyses were repeated 10 times with different seed numbers. The ranking of PELMO input parameters according to their influence on predictions for leaching was stable for the most influential parameters.

Sources of uncertainty in pesticide fate modelling.

There is worldwide interest in the application of probabilistic approaches to pesticide fate models to account for uncertainty in exposure assessments. The first steps in conducting a probabilistic analysis of any system are: (i) to identify where the uncertainties come from; and (ii) to pinpoint those uncertainties that are likely to affect most of the predictions made. This article aims at addressing those two points within the context of exposure assessment for pesticides through a review of the different sources of uncertainty in pesticide fate modelling.

Sensitivity analyses for four pesticide leaching models.

Sensitivity analyses using a one-at-a-time approach were carried out for leaching models which have been widely used for pesticide registration in Europe (PELMO, PRZM, PESTLA and MACRO). Four scenarios were considered for simulation of the leaching of two theoretical pesticides in a sandy loam and a clay loam soil, each with a broad distribution across Europe. Input parameters were varied within bounds reflecting their uncertainty and the influence of these variations on model predictions was investigated for accumulated percolation at 1-m depth and pesticide loading in leachate.

Calibration of pesticide leaching models: critical review and guidance for reporting.

Calibration of pesticide leaching models may be undertaken to evaluate the ability of models to simulate experimental data, to assist in their parameterisation where values for input parameters are difficult to determine experimentally, to determine values for specific model inputs (e.g. sorption and degradation parameters) and to allow extrapolations to be carried out.

Prediction of pesticide concentrations found in rivers in the UK.

SWATCATCH is a distributed model combined with databases within a GIS as the POPPIE system to predict pesticide concentrations in rivers at the catchment outlet. The model was evaluated against a dataset of pesticide concentrations in rivers of England and Wales. More than 2000 individual analyses in each of the years 1995 and 1997 covered approximately 150 catchment-pesticide combinations drawn from 29 catchments and 16 pesticides, themselves selected to represent a range of characteristics and properties.

Sensitivity and first-step uncertainty analyses for the preferential flow model MACRO.

Sensitivity analyses for the preferential flow model MACRO were carried out using one-at-a-time and Monte Carlo sampling approaches. Four different scenarios were generated by simulating leaching to depth of two hypothetical pesticides in a sandy loam and a more structured clay loam soil. Sensitivity of the model was assessed using the predictions for accumulated water percolated at a 1-m depth and accumulated pesticide losses in percolation.